This web map is provides the data and maps used in the story map Population density and diversity in New Zealand, created by Stats NZ. It uses Statistical Area 1 (SA1) data collected and published as part of the 2018 Census. The web map uses a mapping technique called multi-variate dot density mapping. The data used in the map can be found at this web service - 2018 Census Individual part 1 data by SA1.For questions or comments on the data or maps, please contact info@stats.govt.nz Census Data Quality Notes:We combined data from the census forms with administrative data to create the 2018 Census dataset, which meets Stats NZ’s quality criteria for population structure information.We added real data about real people to the dataset where we were confident the people should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.Data quality for 2018 Census provides more information on the quality of the 2018 Census data.An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial Report of the 2018 Census External Data Quality Panel (September 2019), assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. Its second report 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables. In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables.The Quick guide to the 2018 Census outlines the key changes we introduced as we prepared for the 2018 Census, and the changes we made once collection was complete.The geographic boundaries are as at 1 January 2018. See Statistical standard for geographic areas 2018.2018 Census – DataInfo+ provides information about methods, and related metadata.Data quality ratings for 2018 Census variables provides information on data quality ratings.

In a survey about the importance of diversity issues for large organizations in New Zealand conducted in March 2020, ** percent of respondents said that wellbeing was an important diversity issue. Notably, significantly more respondents said that gender and bias were important diversity issues than those from both small and medium organizations.

Dataset contains ethnic group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the ethnic group population count between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

The ethnic groups are:

• European
• Māori
• Pacific peoples
• Asian
• Middle Eastern/Latin American/African
• Other ethnicity.

Map shows percentage change in the census usually resident population count for ethnic groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Ethnicity concept quality rating

Ethnicity is rated as high quality.

Ethnicity – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-998 Not applicable

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

In a 2018 survey about the importance of diversity issues for public sector organizations in New Zealand, **** percent of respondents said that wellbeing was an important diversity issue. On the other end of the scale, only **** percent said that religion was an important diversity issue.

Child protection inequalities are population group differences in contact rates, experiences and outcomes of child protection systems. This article reports rates of Pasifika children’s contact with the statutory child protection system at three outcomes: substantiation, having a family group conference, (FGC) or entering care (placement), and describes intersections between these outcomes and socio-economic deprivation. Including all children resident in Aotearoa New Zealand in 2019–2020 aged 0–17 years, this study compared rates between sole Pasifika, Pasifika plus other ethnicities (Pasifika+), and Non-Māori, Non-Pasifika (NMNP) children. Substantiation was twice as likely for Pasifika, even after controlling for sociodemographic factors, and Pasifika children were 25% more likely to enter care than NMNP children. As socio-economic deprivation increased, rates of substantiation increased for all groups, but most sharply for Pasifika+ children. Sole Pasifika children had the highest rate of substantiation and FGCs in the least deprived quintile of socio-economic deprivation, but the lowest FGC and placement rates in areas of highest deprivation. Pasifika+ children had double the rate of sole Pasifika children for placement in high-deprivation areas, but this was equal to the NMNP rate. Findings are analysed via theories of inequalities. Aggregated ethnic categories obscure considerable variation in within-group experiences.

Introduction: Moyamoya angiopathy (MMA) has been reported in the ethnically diverse Auckland region of New Zealand, but the sociodemographic burden and clinical outcomes remain poorly characterised. This study aims to determine age, sex, and prevalence of MMA stratified by ethnicity, and to assess clinical outcomes in adults residing in Auckland (population 1.9 million).

Methods: A retrospective review of patient records and radiology reports from 2008 to 2025 was conducted using ICD codes and keyword searches. Prevalence was estimated using national census data. Primary outcomes were functional independence (modified Rankin Score 0–2) and the composite of stroke or transient ischaemic attack (TIA). Associations were assessed using univariate and multivariate Cox regression. A pooled analysis of published cohorts was also performed for context.

Results: A total of 100 patients were identified (73% female; mean age 38.5 years, SD 17). Period prevalence was highest among Pacific peoples (11/100,000), followed by Māori (6/100,000), Asians (4/100,000), and Europeans (2/100,000). Overall prevalence increased from 0.8 to 4.5 per 100,000 between 2001 and 2025 (p

New Zealand NZ: Population: Total data was reported at 4,793,900.000 Person in 2017. This records an increase from the previous number of 4,693,200.000 Person for 2016. New Zealand NZ: Population: Total data is updated yearly, averaging 3,291,300.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,793,900.000 Person in 2017 and a record low of 2,371,800.000 Person in 1960. New Zealand NZ: Population: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

Spirogyra is an extremely common, high-biomass, and species-rich green alga of freshwater ecosystems. Conjugating material with zygospores is traditionally required to achieve identification beyond genus. Although filament width classes have been related to habitat status elsewhere, the phylogenetic content of these classes is unknown. We report the results of a survey of 90 sites in New Zealand, designed to determine species distributions in contrasting stream habitats, using rbcL sequencing of microscopically separated filaments to resolve diversity. We found 22 phylospecies of Spirogyra inhabiting 51 sites, with traditional identification using fertile material where possible. Subgeneric resolution was obtained in essentially all samples (vs <30% in most studies relying on fertile material). Four phylospecies were relatively widespread, occurring in 4–10 of the sites examined. Older literature proposed the existence of widespread polyploid serialisation in Spirogyra, with implications for species-level diversity, and we found substantial variation in filament diameter and chloroplasts per cell within phylospecies. Environmental drivers of species distribution, chloroplasts per cell (inferred ploidy), and zygospore formation all differed. Mean summer temperature and pH were the strongest drivers of site occupancy by phylospecies. However, chloroplasts per cell, thought to reflect ploidal state, was mainly driven by habitat enrichment, resulting in larger forms being biased towards lowland waters. A long-standing hypothesis proposes that genome size is limited by nutrient availability, since nucleic acids demand high nitrogen and phosphorus; Spirogyra may offer an excellent experimental system to address this question. The width of zygospores, which are preserved in sediments and found during palaeoecological studies, is correlated with filament diameter and chloroplasts per cell, suggesting that these relationships with trophic status offer potentially useful palaeoecological information. Specimens of narrow (presumably haploid) phylotypes were encountered fertile far more often than were larger phylotypes; whether this reflects polyploidy-related incompatibilities or differences in population turnover rates are unknown.

A brief informal outline of the diversity of Lachnaceae (Leotiomycetes, Helotiales) known for New Zealand, based on PDD and ICMP specimens with DNA sequences. The phylogenetic diversity of the New Zealand taxa is compared with that globally. Asperopilum entry updated 2024.

New Zealand NZ: Population: Growth data was reported at 2.123 % in 2017. This records an increase from the previous number of 2.099 % for 2016. New Zealand NZ: Population: Growth data is updated yearly, averaging 1.129 % from Dec 1960 (Median) to 2017, with 57 observations. The data reached an all-time high of 2.542 % in 1962 and a record low of -0.392 % in 1979. New Zealand NZ: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This dataset contains the digitized treatments in Plazi based on the original journal article Dohlen, Von (2013): Native aphids of New Zealand — diversity and host associations. Zootaxa 3647 (4): 501-517, DOI: 10.11646/zootaxa.3647.4.1

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Changes in the functional structures of communities are rarely examined along multiple large-scale environmental gradients. Here, we describe patterns in functional beta diversity for New Zealand marine fishes vs depth and latitude, including broad-scale delineation of functional bioregions. We derived eight functional traits related to food acquisition and locomotion and calculated complementary indices of functional beta diversity for 144 species of marine ray-finned fishes occurring along large-scale depth (50 - 1200 m) and latitudinal gradients (29° - 51° S) in the New Zealand Exclusive Economic Zone. We focused on a suite of morphological traits calculated directly from in situ Baited Remote Underwater Stereo-Video (stereo-BRUV) footage and museum specimens. We found that functional changes were primarily structured by depth followed by latitude, and that latitudinal functional turnover decreased with increasing depth. Functional turnover among cells increased with increasing depth distance, but this relationship plateaued for greater depth distances (> 750 m). In contrast, functional turnover did not change significantly with increasing latitudinal distance at 700 - 1200 m depths. Shallow functional bioregions (50 - 100 m) were distinct at different latitudes, whereas deeper bioregions extended across broad latitudinal ranges. Fishes in shallow depths had a body shape conducive to efficient propulsion, while fishes in deeper depths were more elongated, enabling slow, energy-efficient locomotion, and had large eyes to enhance vision. Environmental filtering may be a primary driver of broad-scale patterns of functional beta diversity in the deep sea. Greater environmental homogeneity may lead to greater functional homogeneity across latitudinal gradients at deeper depths (700 - 1200 m). We suggest that communities living at depth may follow a ‘functional village hypothesis’, whereby similar key functional niches in fish communities may be maintained over large spatial scales.

Methods Fish community data

Baited Remote Underwear Stereo-Video systems (Stereo-BRUVs) were used to sample marine ray-finned fishes (Class Actinopterygii) in situ at off-shore locations across northern, eastern and southern New Zealand (see Zintzen et al. 2012; 2017 for detailed positions). The Stereo-BRUVs were deployed in a stratified random sampling design at each of seven depths (50 m, 100 m, 300 m, 500 m, 700 m, 900 m and 1200 m) within each of seven locations (from north to south): Rangitāhua, the Kermadec Islands (KER), Three Kings Islands (TKI), Great Barrier Island (GBI), Whakaari, White Island (WI), Kaikōura (KKA), Otago Peninsula (OTA) and the Auckland Islands (AUC) that spanned 21° of latitude in New Zealand waters (with n = 5 - 7 replicate deployments per depth-by-location, see Figure 1 from Zintzen et al. 2017 for a detailed map showing exact sampling locations). Video footage was obtained from a total of 329 deployments (2 hours each) across 47 depth-by-location cells (2 cells were not sampled – White Island at 1200 m and Auckland Islands at 1200 m, due to poor weather conditions). For further details regarding the sampling design, the Stereo-BRUV apparatus and deployment, calibration of measurements and associated methodologies, see Zintzen et al. (2012; 2017).

Functional traits

Fifteen raw morphological measurements were obtained from individuals of each species of fish, in situ, by reviewing footage obtained from each Stereo-BRUV deployment and using the software ‘EventMeasure’ (www.seagis.com.au; see Myers et al. (in press) and Table S1 in Supporting Information). Where possible, measurements from multiple individuals of a single species within a given depth-by-location cell were obtained. A complete set of morphological measurements were not always possible to obtain for every species observed in the video footage. For individuals that were missing no more than 3 (out of 15) measurements, the missing values were imputed using a random-forest machine-learning algorithm (Stekhoven & Bühlmann 2012), based on the other individuals of that species in the dataset having a complete set of measurements. This imputation relies on the assumption that relationships among the morphological variables remain constant within a given species. In addition, to ensure we would have a full set of measured traits for every fish species, we also took raw morphological measurements directly from two preserved museum specimens (held within the National Fish Collection at the Museum of New Zealand Te Papa Tongarewa, Wellington) for every species seen in the video footage (voucher registrations are provided in Table S2 in Supporting Information). In total, there were 144 species recorded across the 47 depth-by-location cells, and 509 species-by-cell occurrences. The original dataset comprised a complete set of 15 raw morphological measurements for 722 individuals observed in video footage (136 of these required some random-forest imputation, and missing traits were remeasured for 4 individuals), plus 291 museum specimens.

We calculated 8 trait variables, namely: eye size, oral gape position, jaw length relative to head length, elongation, eye position, caudal peduncle throttling, pectoral fin position and total body length – each as a function of the 15 raw morphological measurements (2 of the raw morphological measurements were used only for data imputation, Table S3, Supporting Information). These morphological traits focused on key aspects of locomotion, visual perception and feeding for fishes that correspond to important functional variations in the body plan and structure of fishes across large depth gradients (Myers et al. 2019).

We obtained representative trait values for every species within every cell in the study design, while taking into account the intraspecific trait variability. To do so we compiled a table of 8 unique traits (columns) for each species in each depth-by-location cell (509 rows), we randomly drew 1 individual from the list of all complete individuals for each species that were (in order of preference): (i) within that depth-by-location cell, (ii) at the same depth, (iii) from anywhere within the Stereo-BRUV study design or (iv) from a museum specimen. We replicated this random-draw procedure 100 times to generate 100 species-cell × trait (509 × 8) data tables. These data tables enabled us to build 100 multivariate functional spaces based on the 8 normalised continuous trait variables that were used to compute the Euclidean distances between species. By calculating beta diversity values for all 100 tables, then averaging these values, we were able to integrate the available individual-level (within-species) morphological variation into the study, given the logistic constraints on the number of individuals of each species we were able to measure, while also maintaining spatial variation in morphologies encountered within each species as well as possible.

Measures of functional beta diversity

We calculated the functional turnover, or functional beta diversity, by considering the functional distances between each of the species occurring within one cell, with every species occurring within another cell (Swenson 2014). We calculated the following metrics between every pair of cells: (i) mean pairwise functional distance (MPFD.beta) which corresponds in the beta context to the mean distance in functional space between all pairs of species across two cells (Swenson 2014), and (ii) mean nearest neighbor distance (MNND.beta) which corresponds in the beta context to the average of the minimum functional distance between each species in one cell, to every species in another cell (Swenson & Weiser 2014). Previously, MPFD has been defined in an alpha context as the functional analogue to average taxonomic distinctness (Clarke & Warwick 1998), and is also called mean phylogenetic pairwise distance (Swenson 2014) when used in a phylogenetic context. MNND, also called Gamma+ (Clarke et al. 2006), has been used previously in both phylogenetic (Webb et al. 2002) and functional contexts (Swenson & Weiser 2014; Pigot, Trisos & Tobias 2016) where it has been used to estimate functional originality (Mouillot et al. 2013; Leitao et al.2016), and can be considered as an indicator of differences in niche (Swenson et al. 2020). These two functional beta diversity metrics allow the full dimensionality of the functional space to be entirely maintained, which is not necessarily possible with earlier-described metrics, such as convex hulls (Villéger et al. 2013), or hypervolumes (Blonder et al. 2014; 2018). These typically require a rather drastic reduction in dimensionality, especially for species-poor communities such as those encountered in the deep sea.

We calculated the MPFD.beta and MNND.beta metrics between every pair of cells for each of the 100 species-cell by trait (509 × 8) data matrices, then computed the mean and standard deviation across the 100 tables for subsequent analyses. The result was a 47 × 47 matrix of functional dissimilarities (either MPFD.beta or MNND.beta) among all pairs of cells in our study design.

Dataset containing monthly records of bird-flower and bird-fruit visitation collected by Dr KC Burns in Zealandia wildlife reserve in Wellington, New Zealand, observed between January 2006 and April 2015. The datasets are supplementary materials for Vaishnav and Burns (in review, New Zealand Journal of Ecology). The dataset titled "Vaishnav&Burns2025_Dataset_Plants" contains monthly summaries of flower and fruit visitation by native birds to 25 native plant species. The dataset titled "Vaishnav&Burns2025_Dataset_Birds" contains monthly summaries of flower and fruit visitation by eight native bird to native plants. The sheets titled "Summary" in both datasets provide the cumulative monthly counts of bird-flower and bird-fruit visits, and the other sheets provide monthly counts of bird-flower and bird-fruit visits segregated by bird and plant species, respectively. The sheets titled "Not in analyses" are interactions which were recorded in the dataset but excluded form the analysis since there were fewer than three observations for each species per food type (flower or fruit). The dataset titled "Vaishnav&Burns2025_Dataset_Exotics" contains records of flower and fruit visits for non-native birds and plants that were also excluded from the analyses.

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.

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The variables for part 2 of the dataset are:

• Individual home ownership for the census usually resident population count aged 15 years and over
• Usual residence 1 year ago indicator
• Usual residence 5 years ago indicator
• Years at usual residence
• Average years at usual residence
• Years since arrival in New Zealand for the overseas-born census usually resident population count
• Average years since arrival in New Zealand for the overseas-born census usually resident population count
• Study participation
• Main means of travel to education, by usual residence address for the census usually resident population who are studying
• Main means of travel to education, by education address for the census usually resident population who are studying
• Highest qualification for the census usually resident population count aged 15 years and over
• Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
• Highest secondary school qualification for the census usually resident population count aged 15 years and over
• Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
• Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
• Total personal income for the census usually resident population count aged 15 years and over
• Median ($) total personal income for the census usually resident population count aged 15 years and over
• Work and labour force status for the census usually resident population count aged 15 years and over
• Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
• Status in employment for the employed census usually resident population count aged 15 years and over
• Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
• Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
• Industry, by workplace address for the employed census usually resident population count aged 15 years and over
• Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
• Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
• Sector of ownership for the employed census usually resident population count aged 15 years and over
• Individual unit data source.

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Download lookup file for part 2 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value

Abstract

The 2006 Census of Tokelau was conducted on the 19th of October 2006, by both local representatives and Statistics New Zealand staff. Significant planning went into both the collection and output phases of the 2006 Census – with consultation on various aspects of the census (for example, questionnaire content consultation) carried out in Tokelau, Samoa and New Zealand, where appropriate. The 2006 Census questionnaire was based on a standard form developed by the Secretariat of the Pacific Community (SPC), with some changes as appropriate to the Tokelau situation.

Tokelau has a unique population composition. A significant proportion of the Tokelauan population are away from the islands at any one time, for various reasons (e.g. healthcare, education). Considerable time and effort has been put into developing effective population measures for the 2006 Census of Tokelau, with a focus on ensuring all usual residents were counted – in particular those who were not present in Tokelau on census night. Core demographic information was completed by the head of the household, on behalf of people who usually live in Tokelau, but were away on census night.

Geographic coverage

National coverage: the census covers residents of Tokelau and also Tokelau public servants and their families who are employed in Apia, Samoa.

Analysis unit

Individuals and Households.

Universe

The Census covers residents of the non-selfgoverning New Zealand territory of Tokelau and includes Tokelau public servants and their families who are employed in Apia, Samoa. While visitors to Tokelau on Census night are also included, the ultimate aim of the Census is to provide an accurate assessment of the de jure population.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of the 2006 Tokelau Census consisted of three different forms: - Dwelling form: Dwelling characteristics; sources of water; means of cooking; rubbish disposal; hosuehold items; access to Sky TV, internet; numbers of pigs and chickens; sources of income. - Individual form: Individual characteristics; realationship to household head; living where; ethnicity; religion; birth mother and father still alive; language skills: speaking and writing; address 5 years ago; education and qualifications; marital status; paid and unpaid employment; children given birth to. - Absentee form: Individual characteristics; reason for / length of absence; income.

The 2006 Census questionnaire was based on a standard form developed by the Secretariat of the Pacific Community (SPC), with some changes appropriate to the Tokelau situation. Some modifications were made in the 2006 Questionnaire, for instance: - In 2001, the questionnaire only asked for items owned and not necessarily partial or shared ownership as it did in 2006.

A thorough review of the employment and work sections of the questionnaire was undertaken to ensure the unique work force in Tokelau was represented, while at the same time ensuring international comparability. Questions on languages spoken, cigarette smoking and household income were added.

The English version of the questionnaire was reviewed using cognitive testing with four Wellington-based Tokelauan families. The near-final English version of the questionnaire was then tested in Tokelau in July 2006.

Consultation about the content of the census forms was also undertaken in Tokelau and Samoa, with Tokelau government representatives and decision-makers. This consultation was an opportunity to determine what information was required by various data users and how it could best be delivered. After the July visit to Tokelau, the questionnaire was finalised and translation into Tokelauan was carried out. A shorter version of the full individual questionnaire was used for Tokelau Public Service (TPS) working in Samoa, which consisted of only basic demographic questions about each member of the household. No dwelling questions were asked in Samoa.

The census questionnaire was a paperbased, interviewer-administered questionnaire available in two languages (English and Tokelauan).

Cleaning operations

The vast majority of editing was done on the dataset rather than on the raw data. Data evaluation identified cases that needed editing, the resolution was researched by the census manager, and any changes were recorded. This approach was taken to allow for a record to be kept of how many changes (consistency edits) have been undertaken, allowing the process to be defendable.

Census data processing was done using CSPro v 3.0 (Census and Survey Processing System). The Pacific Community (SPC) endorsed CSPro as the standard processing system for all population statistics in the Pacific. For the census of Tokelau, CSPro was used for key entry, editing, and verification of the census data.

Sampling error estimates

Not applicable: Census

Data appraisal

Given the small population size, no post-enumeration survey was done.

In some cases a respondent’s birth day or month was imputed. In these cases the date was randomly selected from one to thirty one and the birth month was randomly selected (from January to December). Any imputed dates were checked to ensure they were valid dates eg not 31 February. Overall the census management team felt that the editing, imputation and mandatory variables methods used were successful. They provided a good balance between quality and practicality for a small census.

Bottlenose dolphins (Tursiops truncatus) occupy a wide range of coastal and pelagic habitats throughout tropical and temperate waters worldwide. In some regions, 'inshore' and 'offshore' forms or ecotypes differ genetically and morphologically, despite no obvious boundaries to interchange. Around New Zealand, bottlenose dolphins inhabit 3 coastal regions: Northland, Marlborough Sounds, and Fiordland. Previous demographic studies showed no interchange of individuals among these populations. Here, we describe the genetic structure and diversity of these populations using skin samples collected with a remote biopsy dart. Analysis of the molecular variance from mitochondrial DNA (mtDNA) control region sequences (n = 193) showed considerable differentiation among populations (Fst = 0.17, Φst = 0.21, P < 0.001) suggesting little or no female gene flow or interchange. All 3 populations showed higher mtDNA diversity than expected given their small population sizes and isolation. To explain the source of this variation, 22 control region haplotypes from New Zealand were compared with 108 haplotypes worldwide representing 586 individuals from 19 populations and including both inshore and offshore ecotypes as described in the Western North Atlantic. All haplotypes found in the Pacific, regardless of population habitat use (i.e., coastal or pelagic), are more divergent from populations described as inshore ecotype in the Western North Atlantic than from populations described as offshore ecotype. Analysis of gene flow indicated long-distance dispersal among coastal and pelagic populations worldwide (except for those haplotypes described as inshore ecotype in the Western North Atlantic), suggesting that these populations are interconnected on an evolutionary timescale. This finding suggests that habitat specialization has occurred independently in different ocean basins, perhaps with Tursiops aduncus filling the ecological niche of the inshore ecotype in some coastal regions of the Indian and Western Pacific Oceans.

This dataset presents the locality, and species diversity measures of Recent benthic foraminifera in shallow water (<50 m) around New Zealand. Census data on 89 species of benthic foraminiferal tests from 131 samples from brackish-water environments throughout New Zealand are analysed by cluster and correspondence analyses. Ten brackish-water faunal associations are recognised. When mapped in study areas they can be seen to inhabit distinct estuarine and tidal inlet environments.

Dataset contains life-cycle age group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the age group population counts between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

The life-cycle age groups are:

• under 15 years
• 15 to 29 years
• 30 to 64 years
• 65 years and over.

Map shows the percentage change in the census usually resident population count for life-cycle age groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Age concept quality rating

Age is rated as very high quality.

Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

In a 2018 survey about ethnicity issues for organizations in New Zealand, **** percent of respondents said that it was an important diversity issue. This represents a notable increase from the previous year's result.